Sketching the Rules of Life: Developing a Model of Biodiversity

November 29, 2018

Charles Darwin meditated on the "tangled bank" of biological diversity in On the Origins of Species in 1859. The seminal work is considered to be the foundation of evolutionary biology, the template upon which scientists for generations have built their investigations into the natural world. Darwin’s theory of evolution does not fully explain the arrangement of life on Earth, however; many questions remain about how genes and environments combine to drive patterns of life. In an age of widespread environmental change, answers to these questions will play a significant role in preserving the world’s biodiversity.

A working group at the Santa Fe Institute recently convened with the aims of furthering ecological and evolutionary theory and crafting an application for a National Science Foundation (NSF) “Rules of Life” grant. Led by Andy Rominger, an SFI Omidyar Fellow, the group met at SFI from November 16-20, 2018. “Our goal is to discover the fundamental processes and universal properties that structure ecological communities,” says Rominger.

The group aims to construct a model of biodiversity that brings together disparate theories from population genetics, community ecology, and macroecology. The proposed model will generate joint predictions of species richness (the number of species found in a defined area), abundance (how common species are), genetic and evolutionary diversity, and trait variation. Such a model could be used to explore whether there are universal laws that govern how ecological communities form. Should universal laws emerge, it could allow scientists to better predict how ecological communities respond to rapidly shifting environmental conditions and identify ecosystems that are particularly sensitive to change.

The full Rules of Life Engine (RoLE) model, as it has been named, will build on the work by Isaac Overcast, one of the working group’s participants and a Ph.D. candidate at the Graduate Center, City University of New York. Overcast and his advisor, Dr. Michael Hickerson (who also attended the meeting), recently developed a simplified model that links species abundance and genetic variation through time, without accounting for the evolution of new species or ecological differences.

“Right now, the way we think about ecological communities is fragmented,” Overcast says. “We track biodiversity in all these different ways, looking at the problem through different lenses that only account for processes acting at specific timescales. Species abundance, genetic variation, and phylogenetic diversity all develop in different ways though. We’re missing something if we don’t broaden our view to account for all these processes interacting simultaneously.”

In addition to the RoLE model, the group is working to develop an informatics platform to help manage and analyze biodiversity data. The platform will enable researchers to standardize data gathered from natural communities around the world to facilitate the application of the RoLE model to their own real-world data.

Initially, the working group will develop the RoLE model using its participants’ data, which includes information about arthropods from the Hawaiian islands, invertebrates of the marine lakes of Palau in Southeast Asia, and diverse vertebrate communities in the Atlantic forest of Brazil. In the long term, the platform should facilitate aggregation and sharing of biodiversity data on a much larger scale.

The group is working on a three- to five-year timeline. The first couple of years will focus on further sussing out evolutionary theory and building the informatics platform, which would be followed by testing hypotheses with field data. This month’s working group made substantial progress on the NSF application and laid the foundation for future work.

“As the saying goes, all models are wrong, but some are useful,” Overcast says. “We can construct this model that nobody would mistake for reality and generate simulated patterns of biodiversity that we can compare to real-world communities,” he continues. "If natural communities broadly conform to the expectations of our model, then we have learned something interesting about nature. Alternatively, if natural communities do not conform, then we have learned something as well."